technical report documentation page 1. report no. 2 ... · 476660-00074-1 9. performing...
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1. Report No. SWUTC/10/476660-00074-1
2. Government Accession No. 3. Recipient's Catalog No.
4. Title and Subtitle
EXAMINING THE ROLE OF TRIP LENGTH IN COMMUTER DECISIONS TO USE PUBLIC TRANSPORTATION
5. Report Date June 2010 6. Performing Organization Code
6. Author(s)
Yao Yu and Randy Machemehl
8. Performing Organization Report No. 476660-00074-1
9. Performing Organization Name and Address Center for Transportation Research University of Texas at Austin 1616 Guadalupe St. Austin, Texas 78701
10. Work Unit No. (TRAIS)
11. Contract or Grant No. DTRT07-G-0006
12. Sponsoring Agency Name and Address Southwest Region University Transportation Center Texas Transportation Institute Texas A&M University System College Station, Texas 77843-3135
13. Type of Report and Period Covered
14. Sponsoring Agency Code
15. Supplementary Notes Supported by a grant from the U.S. Department of Transportation, University Transportation Centers Program 16. Abstract Traveler trip length has for years been used as a fundamental indicator of the best mix of transit modes and user perceptions of travel cost for transit versus auto. This study examines traveler trip lengths across transit modes, work trip duration frequency distributions and mode share distributions in 7 major cities, 8 Combined Statistical Areas and one Metropolitan Statistical Area and found the effect of increasing population and transit mode variety on work trip travel time and travel distance.
A traditional hierarchy of transit modes arranged by traveler trip length might include local bus, light rail, rapid rail (heavy rail) and commuter rail (regional rail). Based on NTD data, the average trip length for these four modes are: local bus (4.6 miles), light rail (3.9 miles), heavy rail (6.3 miles), and commuter rail (30.1).
Trip Time Frequency Distributions for home-based work trips in all major cities selected in this study followed the same pattern except in New York, NY. In virtually all cities from 1990 to 2005, frequencies decreased in all categories less than 30 minutes and increased in categories greater than 30 minutes. Meanwhile, Trip Time Frequency Distributions for home-based work trips in all selected MSAs also followed the same pattern. These results contradicted our assumption that cities or MSAs with different urban forms or transit history might have different Trip Length Frequency Distributions (TLFDs) and showed that at an aggregated level, there is no statistically significant difference among TLFDs for work trips in the selected areas.
Average work trip length for all the 50 MSAs in National Household Travel Survey data also showed that travel time and travel distance for home-based work trips in all selected MSAs are very similar. Also, from the linear regression functions with trip length as dependent variable, it can be seen that work trip time and distance tend to increase with increasing population, work trip time and distance tend to increase also as the number of transit modes increase. 17. Key Words Trip Length, Travel Time, Travel Distance, Work Trip Duration, Transit Modes
18. Distribution Statement No restrictions. This document is available to the public through NTIS: National Technical Information Service 5285 Port Royal Road Springfield, Virginia 22161
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Technical Report Documentation Page
iii
Examining the Role of Trip Length in Commuter Decisions to Use Public Transportation
by
Yao Yu Randy Machemehl
Research Report SWUTC/10/476660-00074-1
Southwest Region University Transportation Center Center for Transportation Research
University of Texas at Austin Austin, Texas
June 2010
iv
ACKNOWLEDGEMENTS The authors recognize that support was provided by a grant from the U.S. Department of Transportation, University Transportation Centers Program to the Southwest Region University Transportation Center.
DISCLAIMER The contents of this report reflect the views of the authors, who are responsible for the facts and the accuracy of the information presented herein. This document is disseminated under the sponsorship of the Department of Transportation, University Transportation Centers Program in the interest of information exchange. The U.S. Government assumes no liability for the contents or use thereof.
v
ABSTRACT
Traveler trip length has, for years, been used as a fundamental indicator of the best mix of
transit modes and user perceptions of travel cost for transit versus auto. This study examines traveler trip lengths across transit modes, work trip duration frequency distributions, and mode share distributions in seven major cities, eight Combined Statistical Areas and one Metropolitan Statistical Area, and found the effect of increasing population and transit mode variety on work trip travel time and travel distance.
A traditional hierarchy of transit modes arranged by traveler trip length might include local bus, light rail, rapid rail (heavy rail) and commuter rail (regional rail). Based on NTD data, the average trip length for these four modes are: local bus (4.6 miles), light rail (3.9 miles), heavy rail (6.3 miles), and commuter rail (30.1).
Trip Time Frequency Distributions for home-based work trips in all major cities selected in this study followed the same pattern except in New York, NY. In virtually all cities from 1990 to 2005, frequencies decreased in all categories less than 30 minutes and increased in categories greater than 30 minutes. Meanwhile, Trip Time Frequency Distributions for home-based work trips in all selected MSAs also followed the same pattern. These results contradicted our assumption that cities or MSAs with different urban forms or transit history might have different Trip Length Frequency Distributions (TLFDs) and showed that at an aggregated level, there is no statistically significant difference among TLFDs for work trips in the selected areas.
Average work trip length for all the 50 MSAs in National Household Travel Survey data also showed that travel time and travel distance for home-based work trips in all selected MSAs are very similar. Also, from the linear regression functions with trip length as dependent variable, it can be seen that work trip time and distance tend to increase with increasing population, work trip time, and distance tend to increase, also, as the number of transit modes increase.
vi
EXECUTIVE SUMMARY
The nation's growth and need to meet mobility, environmental, and energy objectives place great demands on public transit systems. Transit providers are searching for ways to provide sufficient system capacity; however, they are experiencing increasing costs due to fuel price fluctuations and decreasing available funds. This situation means that public transportation providers must adapt their service concepts to maximize the goodness of fit between traveler demands and service types. Despite a significant amount of academic and practitioner-oriented research, the practice of choosing the right transit service to meet current demand and the right development to support transit ridership is still constrained by scarcity of information and data.
Traveler trip length has for years been used as a fundamental indicator of the best mix of transit modes, and user perceptions of travel cost for transit versus auto modes are clearly related to traveler trip length. A traditional hierarchy of transit modes arranged by traveler trip length might include local bus, light rail, rapid rail (heavy rail) and commuter rail (regional rail). Although most service providers would likely agree that traveler trip lengths increase from top to bottom of this list, few would agree regarding the thresholds separating the modes. This research was designed to examine trip length data for these, and sub-sets of these modes, to estimate thresholds among them. This will provide service providers with tools that will enable more direct choices among service types and more optimal design of transit services.
The National Transit Database (NTD) is the Federal Transit Administration's (FTA) national database of statistics for the transit industry. Reported by more than 600 transit agencies across the US, the database includes all modes of public transportation utilized on local and regional routes throughout the country, including private and public buses, heavy and light rail, ferryboats and vanpool service, as well as services for senior citizens and persons with disabilities, and taxi services operated under contract to a public transportation agency.
In this report, operational characteristics from NTD were selected to examine trip length characteristics for four transit modes – local bus, light rail, heavy rail and regional rail – to estimate trip length thresholds among these four transit modes. Also, a variable indicating the variety of available public transit modes for 50 MSAs is formulated based on information from NTD and is used for testing hypotheses regarding work trip length VS public transportation availability and type.
Based on analysis of three datasets – National Transit Dataset, Census Transportation Planning Products and National Household Travel Survey, the following conclusions were developed:
A traditional hierarchy of transit modes arranged by traveler trip length might include local bus, light rail, rapid rail (heavy rail) and commuter rail (regional rail). Based on NTD data, the average trip length for these four modes are: local bus (4.6 miles), light rail (3.9 miles), heavy rail (6.3 miles), and commuter rail (30.1). These results supported the common agreement that traveler trip lengths increase from local bus, light rail, rapid rail to commuter rail.
Trip Time Frequency Distributions for home-based work trips in all major cities selected in this study followed the same pattern except in New York, NY. In all the other major cities, work trips with durations in the 20-29 minutes and 30-34 minutes intervals represent the largest proportions of all work trips, while in New York (NY), work trips in the longer than 60 minutes category are approximately twice as frequent as any other city. Additionally, in virtually all cities from 1990 to 2005, frequencies decreased in all categories less than 30 minutes and increased in categories greater than 30 minutes. Meanwhile, Trip Time Frequency Distributions for home-
vii
based work trips in all selected MSAs also followed the same pattern. These results contradicted our assumption that cities or MSAs with different urban forms or transit history might have different Trip Length Frequency Distributions and showed that at an aggregated level, there is no statistically significant difference among TLFDs for work trips in the selected areas.
Average work trip length for all the 50 MSAs in National Household Travel Survey data also showed that travel time and travel distance for home-based work trips in all selected MSAs are very similar. For all 50 MSAs, average work trip distance ranged from 10.0 to 16.8 miles with an average of 13.0 miles, while travel time for work trips ranged from 18.8 to 32.2 minutes with an average of 23.3 minutes. Also, from the linear regression functions with trip length as dependent variable, it can be seen that work trip time and distance tend to increase with increasing population. Work trip time and distance tend to increase also as the number of transit modes increase.
viii
ix
TABLE OF CONTENTS
Chapter 1 Introduction ................................................................................................................. 1
Chapter 2 Literature Review ........................................................................................................ 3
Chapter 3 Data Sources ............................................................................................................... 5
3.1 National Transit Database (NTD)..................................................................................... 5
3.2 Census Transportation Planning Products (CTPP) ........................................................... 5
3.3 National Household Travel Survey (NHTS) .................................................................... 7
Chapter 4 Analysis Results .......................................................................................................... 9
4.1 Trip length vs Transit Modes ............................................................................................ 9
4.2 Transit Mode Share and Trip Length Frequency Distribution ....................................... 11
4.3 Trip Time Frequency Distribution Hypothesis Testing .................................................. 28
4.4 Trip Length vs Population and Transit Availability ....................................................... 28
Chapter 5 Conclusions ............................................................................................................... 33
REFERENCES ........................................................................................................................... 35
x
LIST OF FIGURES
Figure 4.1 Trip Time Frequency Distributions for Major Cities (1990, 2000 and 2005) ............. 13
Figure 4.2 Trip Length Frequency Distributions for Combined Statistical Areas and Metropolitan Statistical Areas (1990, 2000 and 2005) ........................................................................................ 15
Figure 4.3 Mode Share Distribution for Major Cities (2000 and 2005) ........................................ 18
Figure 4.4 Mode Share Distributions for Combined Statistical Areas and Metropolitan Statistical Areas (2000 and 2005) .................................................................................................................. 20
Figure 4.5 Work Trip Length in Distance with 95% Confidence Interval .................................... 29
Figure 4.6 Work Trip Length in Time with 95% Confidence Interval .......................................... 30
Figure 4.7 Regression of Trip Distance and Trip Duration on Population and TRANSUM ........ 31
LIST OF TABLES
Table 3.1 Study Areas .................................................................................................................... 6
Table 4.1 Average Trip Length in Miles (2006) .......................................................................... 10
Table 4.2 Study Areas .................................................................................................................. 11
Table 4.3 Mode Share for Major Cities in 2000 and 2005 ........................................................... 25
Table 4.4 Trip Length Analysis for Major Cities in 1900, 2000 and 2005 .................................. 26
1
Chapter 1 Introduction
The nation's growth and need to meet mobility, environmental, and energy objectives
place demands on public transit systems. Transit providers are searching for ways to provide
sufficient system capacity; however, they are experiencing increasing costs due to fuel price
fluctuations and decreasing available funds. This situation means that public transportation
providers must adapt their service concepts to maximize the goodness of fit between traveler
demands and service types. Despite a significant amount of academic and practitioner-oriented
research, the practice of choosing the right transit service to meet current demand and the right
development to support transit ridership is still constrained by scarcity of information and data.
Traveler trip length has for years been used as a fundamental indicator of the best mix of
transit modes, and user perceptions of travel cost for transit versus auto modes are clearly related
to traveler trip length. A traditional hierarchy of transit modes arranged by traveler trip length
might include local bus, light rail, rapid rail (heavy rail) and commuter rail (regional rail).
Although most service providers would likely agree that traveler trip lengths increase from top to
bottom of this list, few would agree regarding the thresholds separating the modes. This research
is designed to examine trip length data for these, and sub-sets of these modes, to estimate
thresholds among them. This will provide service providers with tools that will enable more
direct choices among service types and more optimal design of transit services.
Distributions of distances traveled by workers from residential locations to employment
sites are fundamental metrics of urban form. Compact urban forms are considered synonymous
with shorter work trip lengths while low density sprawling urban areas tend to suggest longer
work trips. Similar to perceptions of urban form, availability of mature public transportation
systems is perceived as having a relationship to work trip travel distances, and potentially, to
facilitating urban form. This research is intended to quantify differences in work trip lengths
across cities with different urban forms and a spectrum of available public transportation modes.
That is, it will test hypotheses regarding work trip length versus urban form and work trip length
versus public transportation availability and type.
2
Home-based work trips are more route-fixed, and usually carried out every day during
approximately the same peak hours. So HBW trips can be targeted as the most important market
share by public transportation providers because of their potentially large demand and aversion
toward Peak Hour congestion. This study is focused on home-based work trips in the US and
attempts to identify the hidden characteristics of home-based work trips and thus relationship to
transit demand.
3
Chapter 2 Literature Review
Transit planners and decision makers have been trying for several decades to understand
how urban form, land use and transit interact with each other. A large amount of research has
been conducted on how densities, settlement patterns, land-use compositions, and urban designs
of cities and neighborhoods influence transit usage on an aggregated level. For instance, whether
a future light rail extension will be a cost-effective investment or whether headways should be
increased on a conventional bus route critically depends on whether the built environment and
the people living and working close by will support these changes with their patronage.
The key domestic study on the influence of urban form on transit demand (Pushkarev and
Zupan) focused on the New York metropolitan area and identified significant relationships
among the size and extensiveness of employment centers and transit patronage in corridors
leading to the employment centers. Later, regional planning groups used simulation models to
assess the impact of various growth scenarios on future travel behavior in their regions. Most
find that concentrating jobs and housing where they can be served increases transit mode shares
and reduces vehicle miles traveled.
On an intermediate scale, dense office and residential activity centers generate larger
numbers of transit trips for work and non-work purposes than do less dense, auto-oriented
suburban activity centers. Less dense, less diverse suburban activity centers generate far higher
numbers of vehicle trips and lower levels of auto occupancy, particularly when combined with
abundant, free parking. Residential density and design influence travel behavior directly, but in a
less powerful way than the socioeconomic characteristics of residents. Different types of
households live in dense and spacious areas within metropolitan regions. In American cities,
affluent residents seek space at the metropolitan fringe, while in European cities, affluent
residents often seek amenities and more central locations.
King (2008) estimated the average trip length for several transit modes based on the trip
type they serve in Atlanta, GA and the results showed that the local bus has the shortest average
trip length (4.03 miles). LRT and HRT trip lengths were assumed to be the same length (7.08)
4
and commuter rail has the longest trip length which is 26.8 miles per trip. A traditional hierarchy
of transit modes arranged by traveler trip length might include local bus, light rail, rapid rail
(heavy rail) and commuter rail (regional rail). Although most service providers would likely
agree that traveler trip lengths increase in the listed order, few would agree regarding the
thresholds separating the modes.
Remarkably, little is known concerning trip length variation for a single metropolitan
area at two or more points in time, or for two or more areas at one point in time. Two exceptions
are an early report by the Southeastern Wisconsin Regional Planning Commission [SEWRPC
(1975), Chapter IX] which compared automobile driver trip length by trip purpose, and Bellomo,
Dial and Voorhees (1970) which examined several urban areas at two points in time.
The SEWRPC study indicated little change in the trip length frequency distribution
between 1963 and 1974. Mean travel time of work trips was unchanged at 17.9 min, although
mean travel distance did increase by 0.4 to 5.4 miles.
5
Chapter 3 - Data Sources
3.1 National Transit Database (NTD)
The National Transit Database (NTD) is the Federal Transit Administration's (FTA)
national database of statistics for the transit industry. Reported by more than 600 transit agencies
across the US, the database includes all modes of public transportation utilized on local and
regional routes throughout the country, including private and public buses, heavy and light rail,
ferryboats and vanpool service, as well as services for senior citizens and persons with
disabilities, and taxi services operated under contract to a public transportation agency.
The types of data reported include:
• Operational Characteristics - Vehicle revenue hours and miles, unlinked
passenger trips and passenger miles, etc.
• Service Characteristics - Service reliability and safety, etc.
• Capital Revenues and Assets - Sources and uses of capital, fleet size and age,
and fixed guide ways, etc.
• Financial Operating Statistics - Revenues, Federal, state and local funding, costs, etc.
In this report, operational characteristics from NTD were selected to examine trip length
characteristics for four transit modes – local bus, light rail, heavy rail and regional rail – to
estimate trip length thresholds among these four transit modes. Also, a variable indicating the
variety of available public transit modes for 50 MSAs is formulated based on information from
NTD and is used for testing hypotheses regarding work trip length VS public transportation
availability and type.
3.2 Census Transportation Planning Products (CTPP)
CTPP is a set of special tabulations from decennial census demographic surveys designed
for transportation planners. The CTPP and its predecessor, UTPP, have used data from the
decennial census long form from 1970 to 2000. As the Census Bureau has replaced the decennial
6
census long form with the American Community Survey (ACS), the CTPP 2005 (the latest data
products) is based on the ACS. Because of the large sample size, the data are relatively reliable
and accurate. Selection of study areas is based on the criteria and at least one large city is
included in each of the chosen areas [seven major cities, eight Combined Statistical Areas (CSA)
and one Metropolitan Statistical Area (MSA)). For revealing hidden factors which significantly
affect people’s decisions to choose transit as their travel mode, Trip Length Frequency
Distributions and Mode Share Distributions of home-based work trips were compared over time
and across selected areas by using journey-to-work flow data from CTPP 1900, CTPP 2000 and
CTPP 2005.
Table 3.1 Study Areas
Study Areas
Major cities Combined Statistical Areas Metropolitan Statistical Areas
Chicago, Illinois Atlanta-Sandy Springs-Gainesville, GA-
AL
Miami-Fort Lauderdale-Miami
Beach, Fl
Dallas, Texas Birmingham-Hoover-Cullman, AL
Detroit, Michigan Boston-Worcester-Manchester, MA-NH
Houston, Texas Detroit-Warren-Flint, MI
Los Angeles,
California
Los Angeles-Long Beach-Riverside, CA
New York, New York San Jose-San Francisco-Oakland, CA
Philadelphia,
Pennsylvania
Seattle-Tacoma-Olympia, WA
Washington-Baltimore-Northern Virginia,
DC-MD
7
3.3 National Household Travel Survey (NHTS)
The National Household Travel Survey (NHTS) is a source of national statistics and
trend data on the travel of the American public by all modes of transportation. The 2001 NHTS
is the most recent in the series of these national travel surveys, and it provides information about
travelers and their characteristics, household composition, the amount and type of trips that
people take every day, and selected information about household vehicles. The data include
characteristics of all household trips, by all modes, and all purposes.
Data cases corresponding to 50 MSAs (Metropolitan Statistical Area) from the 2001
NHTS dataset were selected and the average trip length measured both in travel time and travel
distance for home-based work trips within the selected 50 MSAs was tabulated. By comparing
the average commuting trip length among MSAs, the hypothesis regarding relationship between
trip length and urban form compactness was tested. Also, average transit access time and wait
time were calculated based on the NHTS dataset in order to understand the transit accessibility
within the 50 selected MSAs, and furthermore, could be used to produce a meaningful
comparison of travel costs by automobile and transit.
8
9
Chapter 4 Analysis Results
4.1 Trip Length VS Transit Modes
Based on the analysis of the data from National Transit Dataset (NTD), the average trip
length within four major transit modes are shown in Table 4.1. Definitions for transit modes
from NTD is used for this analysis result and is shown as follows.
Metro Bus (MB): A transit mode comprised of rubber-tired passenger vehicles operating on fixed routes and
schedules over roadways. Vehicles are powered by diesel, gasoline, battery, or alternative fuel engines contained
within the vehicle.
Light Rail (LR): A transit mode that typically is an electric railway with a light volume traffic capacity compared to
heavy rail (HR). It is characterized by passenger rail cars operating singly (or in short, usually two car, trains) on
fixed rails in shared or exclusive right-of-way (ROW), low or high platform loading, and vehicle power drawn from
an overhead electric line via a trolley or a pantograph.
Heavy Rail (HR): A transit mode that is an electric railway with the capacity for a heavy volume of traffic. It is
characterized by high speed and rapid acceleration passenger rail cars operating singly or in multi-car trains on
fixed rails, separate rights-of-way (ROW) from which all other vehicular and foot traffic are excluded, sophisticated
signaling, and high platform loading.
Commuter Rail (CR): A transit mode that is an electric or diesel propelled railway for urban passenger train service
consisting of local short distance travel operating between a central city and adjacent suburbs. Service must be
operated on a regular basis by or under contract with a transit operator for the purpose of transporting passengers
within urbanized areas (UZAs), or between urbanized areas and outlying areas. Such rail service, using either
locomotive hauled or self-propelled railroad passenger cars, is generally characterized by multi-trip tickets, specific
station to station fares, railroad employment practices, and usually only one or two stations in the central business
district.
As can be seen in Table 4.1, the average trip length for Local Bus is 4.6 miles, which is
close to that for the Light Rail mode (3.9 miles). Heavy Rail has a longer average trip length (6.3
miles) and Commuter Rail, which usually serves commuting trips between downtown areas and
suburban areas, has the longest average trip length (as high as 30.1 miles). The hierarchy of
average trip length for these four modes is as expected.
10
As far as the standard deviation of trip length in these four transit modes, commuter rail
has a significantly larger variance than the other three modes. Since Commuter Rail service
primarily operates during "peak" travel times, usually the hours of 6:00 a.m. to 9:00 a.m. and
again from 3:00 p.m. to 6:00 p.m., and trains generally run inbound to the city center in the
morning and outbound to suburban areas in the evening. A high variance among nationwide
commuter rail trip lengths for evening was expected. However, when standard deviations are
expressed as functions of their respective means, three of the four modes produce values that are
approximately 0.5. Only heavy rail deviates from this pattern at a value of 0.35.
Table 4.1 Average Trip Length in Miles (2006)
Mean Median Range Standard Deviation
Standard Deviation/
Means Local Bus 4.6 3.9 14.6 2.5 0.54 Light Rail 3.9 3.5 6.4 2.2 0.56
Heavy Rail 6.3 5.8 8.9 2.2 0.35
Commuter Rail 30.1 27.6 59.3 14.8 0.49
Note: While calculating the average trip length for bus mode, those trips with trip length larger
than 15 miles were excluded and the rest were analyzed, since bus trips with more than 15 miles
trip length can be reasonably assumed as long distance commuting trips which are not typically
served by metro bus. And it can be seen that for Metro Bus and Light Rail modes, average trip
length is smaller than that in Heavy rail mode. Among all these four transit modes, Commuter
Rail has the longest average trip length.
11
4.2 Transit Mode Share and Trip Length Frequency Distribution
The Census Transportation Planning Products (CTPP) is a set of special tabulations from
decennial census demographic surveys designed to provide information to reveal characteristics
of home-based work (HBW) trips. And the CTPP 2000 is divided into three parts.
• Part 1 contains residence end data summarizing worker and household
• Part 2 contains place of work data summarizing worker characteristics
• Part 3 contains journey-to-work flow data
We chose large U.S. cities and studied the trip length frequency distributions and mode
share distributions in 7 major cities, 8 Combined Statistical Areas and one Metropolitan
Statistical Area shown in Table 4.2. The data employed for analysis comes from Part 1 of the
CTPP 2000 where frequency distributions of mode to work and travel time to work for residents
in specified areas are tabulated.
Table 4.2 Study Areas
Study Areas
Major cities Combined Statistical Areas Metropolitan Statistical Areas
Chicago, Illinois Atlanta-Sandy Springs-Gainesville, GA-
AL
Miami-Fort Lauderdale-Miami
Beach, Fl
Dallas, Texas Birmingham-Hoover-Cullman, AL
Detroit, Michigan Boston-Worcester-Manchester, MA-NH
Houston, Texas Detroit-Warren-Flint, MI
Los Angeles,
California
Los Angeles-Long Beach-Riverside, CA
New York, New York San Jose-San Francisco-Oakland, CA
Philadelphia,
Pennsylvania
Seattle-Tacoma-Olympia, WA
Washington-Baltimore-Northern Virginia,
DC-MD
characteristics
12
The Trip Length Frequency Distributions in the years 1990, 2000 and 2005 and Mode Share
Distributions in the years 2000 and 2005 for all selected areas are shown as Figures 4.1, 4.2, 4.3
and 4.4.
13
Fig
ure
4.1
Tri
p T
ime
Fre
qu
ency
Dis
trib
uti
ons
for
maj
or c
itie
s (1
990,
200
0 an
d 2
005)
13
14
14
15
Fig
ure
4.2
Tri
p L
engt
h F
req
uen
cy D
istr
ibut
ions
for
Com
bin
ed S
tati
stic
al A
reas
an
d M
etro
pol
itan
Sta
tist
ical
Are
as (
1990
, 200
0 an
d 2
005)
15
16
16
17
17
18
Fig
ure
4.3
Mod
e S
har
e D
istr
ibu
tion
for
maj
or c
itie
s (2
000
and
2005
)
18
19
19
20
Fig
ure
4.4
Mod
e S
har
e D
istr
ibu
tion
s fo
r C
omb
ined
Sta
tist
ical
Are
as a
nd
Met
rop
olit
an S
tati
stic
al A
reas
(20
00 a
nd
200
5)
20
21
21
22
22
23
The data of Figure 4.1 indicate TTFDs in the years 1990, 2000 and 2005 basically
followed the same pattern in all selected major cities except New York City. In all the other
major cities, the frequency of commuting trips increased with their respective trip length
intervals including the less than 5 minutes interval through the 30-44 minutes interval, and those
trips ranging from 20 to 29 minutes and from 30 to 44 minutes account for the top two largest
fractions among all home-based work trips. In New York City, however, trips with trip length
longer than 60 minutes constitute almost the same proportion as those in 30 to 44 minutes, and
trips in 45 to 59 minutes time interval were 15% of all trips, which is significantly higher than
other cities (usually 6% to 8%, Chicago, IL and Philadelphia, PA, with more than 10% of all
trips in the 45-59 minutes time interval).
Across time, the data of Figure 4.1 also indicate: (1) Percentages decreased for categories
up to 30 minutes; (2) Percentages increased for categories greater than 30 minutes; and (3) The
60 minutes or more categories showed the largest increase. All these changes indicate that trip
times increased from 1990 to 2005.
For Combined Statistical Areas and Metropolitan Statistical Areas (Figure 4.2), TLFDs in
the year 1900, 2000 and 2005 have the same pattern as those of the major cities, where the
frequencies of commuting trip categories decreases for categories less than 30 minutes, but
increases for categories greater than 30 minutes. This tends to demonstrate a pattern of
increasing trip times.
For Mode Share Distribution of all major cities in the year 2000 and 2005, the data of
Figure 4.3 indicate decreasing mode share for all categories except Drive Alone. New York City
is the exception in that the Drive Alone share decreased, while bus and other transit mode shares
increased. Also, in New York City, other transit modes (excluding local bus) have the largest
mode share, while for all the other major cities, Drive Alone is the dominant mode for
commuting trips, and usually accounts for an approximate 70% mode share. Chicago and
Philadelphia have smaller Drive Alone mode shares (about 50%) and larger transit mode shares,
likely reflecting greater transit availability.
For Combined Statistical Areas and Metropolitan Statistical Area (Figure 4.4), Mode
Share Distributions in the years 2000 and 2005 have the same pattern as those of most major
24
cities, where Drive Alone has the dominant mode share. It increased over the five-year period.
Since these data reflect characteristics of an area larger than the respective city, in every case,
carpool has the next largest mode share after Drive Alone.
Detailed Mode Share information is listed in Table 4.3 where mode share of “Drive
Alone” and “Transit” for specified areas are listed across time. Among those major cities, New
York, NY has the largest transit share (52.6% in 2000, 56.7% in 2005) with Chicago, IL (25.9%
in 2000, 26.0% in 2005) and Philadelphia, Pa (25.7% in 2000, 26.6% in 2005) ranked next to it.
All three cities have a good reputation and long history for being transit-oriented, and besides
local bus, they all provide rail transit modes, which are in another way showing their transit-
friendliness in terms of transit mode diversity. Meanwhile, among those Combined Statistical
Areas and Metropolitan Statistical Areas, San Jose-San Francisco-Oakland, CA, Washington-
Baltimore-Northern Virginia, DC-MD and Boston-Worcester-Manchester, MA-NH have larger
transit shares than other CSAs and MSAs. Also, these three CSAs are well-known for resident
positive perception of public transportation.
25
Table 4.3 Mode Share for Major Cities in 2000 and 2005
Mode Share for Major Cities in 2000 and 2005
Mode Share in percentage (%) 2000 2005
Drive Alone Transit Drive Alone Transit
Chicago, IL 51.3 25.9 54.9 26.0
Dallas, TX 72.8 5.5 76.2 4.5
Detroit, MI 69.9 8.3 78.3 7.3
Houston, TX 73.6 5.9 76.5 5.2
Los Angeles, CA 68.6 10.5 71.2 10.8
New York, NY 25.6 52.6 24.5 56.7
Philadelphia, PA 50.1 25.7 52.8 26.6
Mode Share for Combined Statistical Areas in 2000 and 2005
Mode Share in percentage (%) 2000 2005
Drive Alone Transit Drive Alone Transit
Atlanta-Sandy Springs-Gainesville, GA-AL 79.7 3.3 82.9 3.3
Birmingham-Hoover-Cullman, AL 85.0 0.7 86.2 0.5
Boston-Worcester-Manchester, MA-NH 76.2 9.1 77.6 8.7
Detroit-Warren-Flint, MI 86.1 1.8 87.9 1.6
Los Angeles-Long Beach-Riverside, CA 75.1 4.8 77.9 4.7
San Jose-San Francisco-Oakland, CA 71.0 9.7 72.9 9.5
Seattle-Tacoma-Olympia, WA 74.8 6.9 76.0 6.9
Washington-Baltimore-Northern Virginia, DC-MD 72.9 9.5 73.3 11.0
Mode Share for Metropolitan Statistical Areas in 2000 and 2005
Mode Share in percentage (%) 2000 2005
Drive Alone Transit Drive Alone Transit
Miami-Fort Lauderdale-Miami Beach, Fl 79.8 3.3 81.9 3.7
Table 4.4 suggests that among the selected major cities, the three most transit-oriented
cities, which are New York (NY), Chicago (IL) and Philadelphia (PA), have the largest
proportion of work trips with travel times greater than one hour. This relationship between a
well-developed transit system and longer work trip times can be explained in two ways. On one
26
hand, since transit users usually spend more time to access and wait for transit, the transit mode
is relatively more time-consuming than the auto mode. Also, in these three cities more work trips
occurred by transit and the proportion of longer work trips increased. On the other hand, since
congestion in these cities might be severe, travel time for work trips is longer than that in other
cities. Therefore, residents choose to use transit instead of auto mode in order to avoid the
congested auto system.
However, the relationship between mode share and trip time for work trips in CSAs and
MSAs is not clear. Across time, the top three CSAs and MSAs with larger proportions of long
duration work trips (one hour or longer) include both transit-oriented areas - Washington-
Baltimore-Northern Virginia (DC-MD); San Jose-San Francisco-Oakland (CA); and auto-
oriented areas - Atlanta-Sandy Springs-Gainesville (GA-AL); Los Angeles-Long Beach-
Riverside (CA). In addition, the fraction of one hour or longer trips increased across time in all
areas except San Jose-San Francisco-Oakland (CA).
Table 4.4 Trip length analysis for Major Cities in 1900, 2000 and 2005
Trip length analysis for Major Cities in 1900, 2000 and 2005
Percentage (%) for work trips
with
1990 2000 2005
> 20
min
>30
min
>60
min
> 20
min
>30
min
>60
min
> 20
min
>30
min
>60
min
Chicago, IL 73.9 55.5 13.1 76.3 58.9 16.0 75.2 57.1 15.3
Dallas, TX 60.8 36.0 4.6 63.1 40.0 6.8 62.5 38.6 6.1
Detroit, MI 62.7 35.4 5.7 66.5 39.6 8.1 65.0 38.1 6.5
Houston, TX 60.9 38.4 5.7 63.4 41.9 7.5 63.3 40.4 7.9
Los Angeles, CA 63.7 42.5 8.1 66.4 45.5 10.6 68.2 47.9 11.6
New York, NY 77.3 63.5 22.8 79.6 65.5 24.5 80.7 66.3 24.8
Philadelphia, PA 66.8 46.0 8.4 70.2 50.6 12.7 71.6 50.9 11.9
27
Trip length analysis for Combined Statistical Areas in 1900, 2000 and 2005
Percentage (%) for work trips with
1990 2000 2005
>20 min
>30
min
>60
min
> 20
min
>30
min
>60
min
> 20
min
>30
min
>60
min
Atlanta-Sandy Springs-Gainesville,
GA-AL 66.6 46.3 10.0 70.0 50.3 12.9 70.2 51.1 14.3
Birmingham-Hoover-Cullman, AL 57.8 34.4 5.2 62.1 39.2 7.2 62.6 39.4 7.1
Boston-Worcester-Manchester,
MA-NH 54.6 35.3 6.4 61.1 41.9 9.8 62.3 43.2 10.4
Detroit-Warren-Flint, MI 56.6 32.6 4.6 60.5 37.4 6.6 61.0 38.1 6.4
Los Angeles-Long Beach-Riverside,
CA 59.9 40.2 9.5 62.6 42.7 11.1 63.4 43.6 12.1
San Jose-San Francisco-Oakland,
CA 58.5 38.4 8.2 63.3 43.8 11.9 60.7 40.1 9.5
Seattle-Tacoma-Olympia, WA 57.6 34.9 6.0 61.8 40.1 9.1 62.0 40.5 8.9
Washington-Baltimore-Northern
Virginia, DC-MD 66.6 46.3 10.0 70.0 50.3 12.9 70.2 51.1 14.3
Trip length analysis for Metropolitan Statistical Areas in 1900, 2000 and 2005
Percentage (%) for work trips
with
1990 2000 2005
> 20
min
>30
min
>60
min
> 20
min
>30
min
>60
min
> 20
min
>30
min >60 min
Miami-Fort Lauderdale-Miami
Beach, Fl 58.6 36.0 4.5 64.7 43.5 8.3 67.1 47.1 9.5
4.3 Trip Time Frequency Distribution Hypothesis Testing
In this section, Chi-square tests were performed to test the null hypotheses: (1) Trip Time
Frequency Distributions(TTFD) for all areas for two time periods (2000 and 2005) follow the
same patterns; and (2) Trip Time Frequency Distributions (TTFD) for two or more areas at the
same time point follow the same patterns. It is usually assumed that cities or MSAs with highly
28
developed transit systems tend to be more compact and therefore, TTFD in these areas would
have fewer long duration trips (that is a shorter tail on the right-hand side) compared to the other
areas. However, the results showed that the null hypotheses cannot be rejected at the 95%
confidence level, which means that statistically, in the years 2000 and 2005, work trips in all the
areas studied shared similar Trip Time Frequency Distributions. Additionally, for all specified
areas, the Trip Time Frequency Distributions follow the same patterns across time. This may be
due to the fact that people try to manage their commuting travel time by considering the same
factors, including available transportation modes and congestion.
4.4 Trip Length VS Population and Transit Availability
Average trip length for home-based work trips measured both in time and distance with
the NHTS 2001 data were tabulated for 50 MSAs (Appendix 1) in the US. Also, a new variable
TRANSUM was calculated from the NTD data which indicates how many transit modes are
available in each respective MSA. To get the TRANSUM variable, first three dummy variables
were created indicating whether in the specified MSA there is Light Rail Service, Heavy Rail
Service or Commuter Rail Service, and then generated the TRANSUM variable by adding up the
three dummy variables. So, for TRANSUM, “0” means there is only bus service and the larger
the value for TRANSUM, the more available transit modes. Together with the TRANSUM
variable, population data for 2000 from Census is introduced in the national dataset.
In Figure 4.5, each blue line shows the average trip distance for home-based work trips in
the respective MSA’s, and populations of the MSAs increase from left to right. The green and
red plots (spanning 2 standard deviations around the means) represent the 95% confidence
interval about the means. In Figure 4.6, the travel time information for home-based work trips is
shown. Travel time and travel distance for home-based work trips in all selected MSAs vary only
slightly. For all these 50 MSAs, average trip distance for work trips in the 50 MSAs ranged from
10.0 to 16.8 miles with an average of 13.0 miles, while travel time for work trips ranged from
18.8 to 32.2 minutes, with an average of 23.3 minutes.
It is noticed, both in Figure 4.5 and Figure 4.6, that the first half of the MSA’s followed
almost the same pattern. The mean travel distance and travel time for work trips were almost
constant in those MSAs, and variance for travel time and travel distance follow the same pattern.
29
Standard deviations of travel distance increase with standard deviations of travel time. However,
the second half of the MSA’s was different from that of the first half. First, average travel
distance for work trips in those MSAs was almost the same as the first half, but average travel
time for work trips increased with population in these MSAs. Secondly, standard deviations for
work trip travel time in those MSAs increased slightly with population, but standard deviations
for travel distance in the second half of the MSAs seemed to have no pattern.
Figure 4.5 Work Trip length in Distance with 95% confidence interval
30
Figure 4.6 Work Trip Length in Time with 95% confidence interval
The identification of a relationship between Population and Transit availability versus
work trip length was investigated. Linear regression analyses were performed to find a
relationship between work trip length versus population and transit availability.
From Figure 4.7, mean travel time for work trips increase with population and the
TRANSUM variable which means for MSAs with larger populations, people tend to travel
longer distances for work trips. For MSAs with more transit modes, people are likely to travel
greater distances and times to work. With increasing population, people tend to spend more time
and distance for home-based work trips, and with more transit modes available in their MSA,
people are likely to have longer duration and longer distance work trips. It is as expected, since
MSAs with larger population usually have larger land area, correspondingly, the fraction of
residents that need to or have to travel further is higher than in MSAs with smaller populations.
Therefore, the proportion of longer distance work trips is also higher in MSAs with larger
population. Similarly, if more transit, especially rail service, is available, more commuters are
willing to live in suburban areas and endure longer work trip travel distances and times.
31
Figure 4.7 Regression of Trip Distance and Trip Duration on Population and TRANSUM
32
33
Chapter 5 Conclusions
Based on the analysis of three datasets – National Transit Dataset, Census Transportation
Planning Products and National Household Travel Survey, the following conclusions were
developed:
A traditional hierarchy of transit modes arranged by traveler trip length might include
local bus, light rail, rapid rail (heavy rail) and commuter rail (regional rail). Based on NTD data,
the average trip length for these four modes are: local bus (4.6 miles), light rail (3.9 miles),
heavy rail (6.3 miles), and commuter rail (30.1). This result supports the common belief that
traveler trip lengths increase from local bus, light rail, rapid rail to commuter rail.
Trip Time Frequency Distributions for home-based work trips in all major cities selected
in this study followed the same pattern, except in New York, NY. In all the other major cities,
work trips with durations in the 20-29 and 30-34 minute intervals represent the largest
proportions of all work trips, while in New York (NY), work trips in the longer than 60 minute
category are approximately twice as frequent as in any other city. Additionally, in virtually all
cities from 1990 to 2005, frequencies decreased in all categories less than 30 minutes and
increased in categories greater than 30 minutes. Meanwhile, Trip Time Frequency Distributions
for home-based work trips in all selected MSAs also followed the same pattern. These results
contradicted our assumption that cities or MSAs with different urban forms or transit history
might have different Trip Length Frequency Distributions, and showed that at an aggregated
level, there is no statistically significant difference among TLFDs for work trips in the selected
areas.
Average work trip length for all the 50 MSAs in the National Household Travel Survey
data also showed that travel time and travel distance for home-based work trips in all selected
MSAs are very similar. For all 50 MSAs, average work trip distance ranged from 10.0 to 16.8
miles with an average of 13.0 miles, while travel time for work trips ranged from 18.8 to 32.2
minutes with an average of 23.3 minutes. Also, from the linear regression functions with trip
length as dependent variable, work trip time and distance tends to increase with increasing
34
population. Work trip time and distance tend to increase, also, as the number of transit modes
increase.
35
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